Reasoning goals and representational decisions in computational cognitive neuroscience: lessons from the drift diffusion model

Computational cognitive models are powerful tools for enhancing the quantitative and theoretical rigor of cognitive neuroscience. It is thus imperative that model users—researchers who develop models, use existing models, or integrate model-based findings into their own research—understand how these tools work and what factors need to be considered when engaging with them. To this end, we have developed a philosophical toolkit that address core questions about computational cognitive models in the brain and behavioral sciences. Drawing on recent advances in philosophy of modeling, we highlight the central role of model users’ reasoning goals in the application and interpretation of formal models. We demonstrate the utility of this perspective by first offering a philosophical introduction to the highly popular drift diffusion model (DDM) and then providing a novel conceptual analysis of a long-standing debate within that model’s literature. Contrary to most existing work, our analysis suggests that the two models implicated in the debate offer complementary—rather than competing—explanations of speeded choice behavior. We achieve this by first explicating the role of optimality in model-based explanations, and then demonstrating how the two different models reflect different commitments to optimality explanations. We use these insights to offer a principled heuristic for when to use one form versus the other, before concluding with a critical appraisal of reasoning goals in formal model comparison. Altogether, we demonstrate the conceptual and practical utility of philosophy for inspiring new directions in brain and behavioral research.